.png){kind=icon}
返回
Active cluster annotation for wafer map pattern classification in semiconductor manufacturing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.115429.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In semiconductor manufacturing, wafer map pattern classification (WMPC) is important for ensuring good manufacturing quality because the defect type on a wafer map provides important information for determining defect causes. To construct a high-performance WMPC model, a large amount of labeled training data is required, which entails a high annotation cost for engineers. To reduce the annotation cost, an active learning framework has been investigated, in which annotation is conducted at the individual wafer map level. If wafer maps can be grouped into clusters based on the similarity of defect patterns, then annotation can be performed at the cluster level rather than at the wafer map level, thereby affording cost effectiveness. Based on the cluster-level annotation, we propose an active cluster annotation to obtain a high-performance WMPC model with reduced annotation cost. For a dataset annotated only for a small subset of wafer maps, clustering is first conducted for unlabeled wafer maps. In an active learning iteration, a convolutional neural network (CNN) is constructed with labeled wafer maps. Subsequently, the cluster-level classification uncertainties of the CNN for unlabeled wafer maps are calculated. With the uncertainties, the query clusters are selected and annotated by an engineer at the cluster level. The performance of the CNN is improved cost-effectively by repeating these iterations. We demonstrate the effectiveness of the proposed method through experiments on real-world data from a semiconductor manufacturer. In addition, we show that cluster-level annotation is a robust annotation method that can yield consistent labels.
Keyword:
Wafer map pattern classification
Active learning
Uncertainty estimation
Cluster-level annotation
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
An Observational Study on the Humoral and Cellular Immune Response to SARS-CoV-2 mRNA Vaccination in Multiple Sclerosis and Other Autoimmune Neurological Disorders Treated With Anti-CD20 Therapies.png)
Neurology
OAAI
NeurologyIF0
OAAI分享收藏Separation of composite defect patterns on wafer bin map using support vector clustering使用支持向量聚类在晶圆bin图上分离复合缺陷图案
PREAI
分享收藏Diazepam in Labour: Its Metabolism and Effect on the Clinical Condition and Thermogenesis of the NewbornBMJ
OAAI
BMJIF0
OAAI分享收藏Stacked convolutional sparse denoising auto-encoder for identification of defect patterns in semiconductor wafer map堆叠卷积稀疏去噪自动编码器在半导体晶片地图缺陷模式识别中的应用
PREAI
分享收藏
查看更多内容